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Large scale summarization using ensemble prompts and in context learning approaches
(Springer Science and Business Media LLC, 2025-03-25)
Andrés Leiva-Araos
;
Bady Gana
;
Héctor Allende-Cid
;
José García
;
Manob Jyoti Saikia
5
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Leveraging LLMs for Efficient Topic Reviews
(2024)
Bady Gana
;
Andrés Leiva-araos
;
Héctor Allende-Cid
;
José García
<jats:p>This paper presents the topic review (TR), a novel semi-automatic framework designed to enhance the efficiency and accuracy of literature reviews. By leveraging the capabilities of large language models (LLMs), TR addresses the inefficiencies and error-proneness of traditional review methods, especially in rapidly evolving fields. The framework significantly improves literature review processes by integrating advanced text mining and machine learning techniques. Through a case study approach, TR offers a step-by-step methodology that begins with query generation and refinement, followed by semi-automated text mining to identify relevant articles. LLMs are then employed to extract and categorize key themes and concepts, facilitating an in-depth literature analysis. This approach demonstrates the transformative potential of natural language processing in literature reviews. With an average similarity of 69.56% between generated and indexed keywords, TR effectively manages the growing volume of scientific publications, providing researchers with robust strategies for complex text synthesis and advancing knowledge in various domains. An expert analysis highlights a positive Fleiss’ Kappa score, underscoring the significance and interpretability of the results.</jats:p>
Scopus© Citations 2 1
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Transcriptome Data Analysis Applied to Grapevine Growth Stage Identification
(2024)
Francisco Altimiras
;
Leonardo Pavéz
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Alireza Pourreza
;
Osvaldo Yañez
;
Lisdelys González-Rodríguez
In agricultural production, it is fundamental to characterize the phenological stage of plants to ensure a good evaluation of the development, growth and health of crops. Phenological characterization allows for the early detection of nutritional deficiencies in plants that diminish the growth and productive yield and drastically affect the quality of their fruits. Currently, the phenological estimation of development in grapevine (Vitis vinifera) is carried out using four different schemes: Baillod and Baggiolini, Extended BBCH, Eichhorn and Lorenz, and Modified E-L. Phenological estimation requires the exhaustive evaluation of crops, which makes it intensive in terms of labor, personnel, and the time required for its application. In this work, we propose a new phenological classification based on transcriptional measures of certain genes to accurately estimate the stage of development of grapevine. There are several genomic information databases for Vitis vinifera, and the function of thousands of their genes has been widely characterized. The application of advanced molecular biology, including the massive parallel sequencing of RNA (RNA-seq), and the handling of large volumes of data provide state-of-the-art tools for the determination of phenological stages, on a global scale, of the molecular functions and processes of plants. With this aim, we applied a bioinformatic pipeline for the high-throughput quantification of RNA-seq datasets and further analysis of gene ontology terms. We identified differentially expressed genes in several datasets, and then, we associated them with the corresponding phenological stage of development. Differentially expressed genes were classified using count-based expression analysis and clustering and annotated using gene ontology data. This work contributes to the use of transcriptome data and gene expression analysis for the classification of development in plants, with a wide range of industrial applications in agriculture.
Scopus© Citations 2 1
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Relevance of Machine Learning Techniques in Water Infrastructure Integrity and Quality: A Review Powered by Natural Language Processing
(2023)
José García
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LEIVA ARAOS, ANDRÉS
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Emerson Diaz-Saavedra
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Paola Moraga
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Hernan Pinto
Water infrastructure integrity, quality, and distribution are fundamental for public health, environmental sustainability, economic development, and climate change resilience. Ensuring the robustness and quality of water infrastructure is pivotal for sectors like agriculture, industry, and energy production. Machine learning (ML) offers potential for bolstering water infrastructure integrity and quality by analyzing extensive data from sensors and other sources, optimizing treatment protocols, minimizing water losses, and improving distribution methods. This study delves into ML applications in water infrastructure integrity and quality by analyzing English-language articles from 2015 onward, compiling a total of 1087 articles. Initially, a natural language processing approach centered on topic modeling was adopted to classify salient topics. From each identified topic, key terms were extracted and utilized in a semi-automatic selection process, pinpointing the most relevant articles for further scrutiny, while unsupervised ML algorithms can assist in extracting themes from the documents, generating meaningful topics often requires intricate hyperparameter adjustments. Leveraging the Bidirectional Encoder Representations from Transformers (BERTopic) enhanced the study’s contextual comprehension in topic modeling. This semi-automatic methodology for bibliographic exploration begins with a broad topic categorization, advancing to an exhaustive analysis of each topic. The insights drawn underscore ML’s instrumental role in enhancing water infrastructure’s integrity and quality, suggesting promising future research directions. Specifically, the study has identified four key areas where ML has been applied to water management: (1) advancements in the detection of water contaminants and soil erosion; (2) forecasting of water levels; (3) advanced techniques for leak detection in water networks; and (4) evaluation of water quality and potability. These findings underscore the transformative impact of ML on water infrastructure and suggest promising paths for continued investigation.
Scopus© Citations 12 1
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A Robust Statistical Methodology for Measuring Enterprise Agility
(2023)
Roberto Moraga-Díaz
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Andrés Leiva-Araos
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José García
<jats:p>In an era characterized by rapid technological advancements, economic fluctuations, and global competition, adaptability and resilience have become critical success factors for businesses navigating uncertainty and complexity. This article explores the role of enterprise agility in today’s business landscape at Latam branch of Tata Consultancy Services, where organizations face complex and diverse operations. We aim to examine how companies can become more agile in the face of emerging challenges and seize opportunities swiftly to drive growth and deliver value. Since 2014, the division has embarked on an agile transformation journey to drive growth, deliver value, foster innovation, and build resilience in an increasingly dynamic environment. We scrutinize an approach to measuring and enhancing enterprise agility, employing statistical analysis and continuous improvement methodologies to tackle real-world challenges while offering valuable insights and recommendations for organizations aiming to implement similar systems. The results of an agile transformation in a certain company’s Latam branch serve as a compelling case study, demonstrating how the implementation of targeted measures and continuous improvement can significantly bolster enterprise agility. Methodologically, our work applies a novel sequence of parametric statistical tests which, to the best of our knowledge, have not been used in the industry to validate the results of business agility metrics. In future work, we aim to create a new workflow considering non-parametric tests to address data with other statistical distributions. We conclude our work by proposing a sequence of steps for organizations to implement business agility metrics.</jats:p>
19Scopus© Citations 5
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Exploring Initialization Strategies for Metaheuristic Optimization: Case Study of the Set-Union Knapsack Problem
(2023)
José García
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Andres Leiva-Araos
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Broderick Crawford
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Ricardo Soto
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Hernan Pinto
In recent years, metaheuristic methods have shown remarkable efficacy in resolving complex combinatorial challenges across a broad spectrum of fields. Nevertheless, the escalating complexity of these problems necessitates the continuous development of innovative techniques to enhance the performance and reliability of these methods. This paper aims to contribute to this endeavor by examining the impact of solution initialization methods on the performance of a hybrid algorithm applied to the set union knapsack problem (SUKP). Three distinct solution initialization methods, random, greedy, and weighted, have been proposed and evaluated. These have been integrated within a sine cosine algorithm employing k-means as a binarization procedure. Through testing on medium- and large-sized SUKP instances, the study reveals that the solution initialization strategy influences the algorithm’s performance, with the weighted method consistently outperforming the other two. Additionally, the obtained results were benchmarked against various metaheuristics that have previously solved SUKP, showing favorable performance in this comparison.
32Scopus© Citations 4
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Sensing Urban Patterns with Antenna Mappings: The Case of Santiago, Chile
(2016)
Eduardo Graells-garrido
;
Oscar Peredo
;
José García
7Scopus© Citations 57

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